Different SP online car-hailing driver authority control method and system

Abstract

The present invention provides a method and system for controlling the authority of drivers of different SP online car-hailing vehicles, which relates to the technical field of the authority control of online car-hailing vehicles. The specific steps include: S1. Collecting the driver behavior parameters and the marketing behavior parameters of the driver participating in the platform from the historical order data of N online car-hailing vehicles, the driver behavior parameters include the order acceptance rate, punctuality rate and user evaluation coefficient, the marketing behavior parameters include the activity participation rate and platform activity, and respectively establishing the driver's behavior parameter data set and the marketing behavior parameter data set; S2. Dedimensionalizing the driver behavior parameter data set and the marketing behavior parameter data set, respectively, and performing correlation analysis. Therefore, the present invention enables the system to more accurately evaluate the behavior performance of each driver by collecting and analyzing the driver's behavior parameter data and the marketing behavior parameters of the participating platform, and to perform fair and accurate driver authority allocation according to the behavior performance of each driver.

Description

Technical Field

[0001] The present invention relates to the technical field of online car-hailing driver authority control, and in particular to a method and system for controlling the authority of different SP online car-hailing drivers. Background Art

[0002] The SP online car-hailing driver authority control method refers to the operations that drivers who register on and provide services on a specific online car-hailing service provider (SP) platform can perform and the rights they can obtain on the platform, and through a combination of software and hardware, effectively manage and allocate the authority of SP online car-hailing drivers.

[0003] In the prior art, the authority control of online ride-hailing drivers is usually based on a passenger rating system, which evaluates the driver's service quality through passenger feedback and ratings. The platform then adjusts the driver's authority based on the rating results, rewards drivers with outstanding performance, or takes corresponding authority control measures for drivers with poor performance.

[0004] However, there are still the following shortcomings: From the above statements, it can be seen that the passenger's rating system usually includes the driver's punctuality rate and user evaluation coefficient, etc., but the authority level of the online car-hailing driver is not only related to the passenger's rating system, but also related to the driver's enthusiasm for participating in platform marketing activities. When only considering the passenger's rating system's authority control over the online car-hailing driver and ignoring the platform marketing behavior in which the online car-hailing driver participates, the rating of the online car-hailing driver will be inaccurate and comprehensive, resulting in some drivers who perform well in the passenger's rating system but do not actively participate in platform marketing activities being overestimated, and some drivers who actively participate in platform marketing activities but perform averagely in the passenger's rating system being underestimated, resulting in unfair distribution of authority.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] The purpose of the present invention is to provide a method and system for controlling the authority of different SP online car-hailing drivers to solve the problems raised in the above background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A method for controlling the authority of different SP online car-hailing drivers, the specific steps comprising:

[0009] S1. Collect driver behavior parameters and marketing behavior parameters of drivers participating in the platform from historical order data of N online ride-hailing vehicles, where the driver behavior parameters include order acceptance rate, punctuality rate and user evaluation coefficient, and the marketing behavior parameters include activity participation rate and platform activity, and establish driver behavior parameter data sets and marketing behavior parameter data sets respectively;

[0010] S2. Dedimensionalizing the driver behavior parameter data set and the marketing behavior parameter data set, and performing correlation analysis to generate a driver behavior coefficient and a marketing behavior coefficient, respectively;

[0011] S3. Dedimensionalize the driver behavior coefficient and the marketing behavior coefficient, and perform correlation analysis to generate a comprehensive coefficient for characterizing the authority level of the online car-hailing driver, and compare the comprehensive coefficient of the online car-hailing driver with a set comprehensive coefficient threshold to determine the interval range of the comprehensive coefficient of the online car-hailing driver and the corresponding assigned authority level;

[0012] S4. Obtain corresponding permissions according to the permission level assigned to the online car-hailing driver.

[0013] Furthermore, the collected acceptance rate, punctuality rate and user evaluation coefficient of the online car-hailing driver are processed in a dimensionless manner and subjected to correlation analysis to generate the driver behavior coefficient. , based on the following formula:

[0014] ;

[0015] in, is the acceptance rate of online car-hailing drivers, The punctuality rate of online ride-hailing drivers, is the user evaluation coefficient of online car-hailing drivers, is the factor coefficient of the online car-hailing driver’s order acceptance rate, is the factor coefficient of the punctuality rate of online car-hailing drivers, is the factor coefficient of the user evaluation coefficient of the online car-hailing driver, , =1.42, is a constant correction factor.

[0016] Furthermore, the collected data on the online car-hailing drivers’ activity participation rate and platform activity are undimensionalized and subjected to correlation analysis to generate a marketing behavior coefficient. , based on the following formula:

[0017] ;

[0018] in, is the activity participation rate of online car-hailing drivers, The platform activity of online ride-hailing drivers. is the factor coefficient of the activity participation rate of online car-hailing drivers, is the factor coefficient of the platform activity of online car-hailing drivers, , =1.06, is a constant correction factor.

[0019] Furthermore, the driver behavior coefficient of the online car-hailing vehicle and marketing behavior coefficient Dedimensionalize the data and conduct correlation analysis to generate a comprehensive coefficient for characterizing the authority level of online car-hailing drivers , based on the following formula:

[0020] ;

[0021] in, is the factor coefficient of the driver behavior coefficient of online car-hailing, is the factor coefficient of the marketing behavior coefficient of online car-hailing, , =1.1.

[0022] Furthermore, the comprehensive coefficient of the online car-hailing driver and the set comprehensive coefficient threshold In comparison, the process of determining the interval range of the comprehensive coefficient of the online car-hailing driver and the corresponding allocated authority is as follows:

[0023] when , then the online car-hailing driver is assigned low-level permissions;

[0024] when , then the online car-hailing driver is assigned the middle-level authority;

[0025] when , then high-level permissions will be assigned to the online car-hailing driver.

[0026] Furthermore, according to the authority level assigned to the online car-hailing driver, the process of obtaining the corresponding authority is as follows:

[0027] When online ride-hailing drivers have low-level permissions, they will receive relatively low order dispatch priority and incentives;

[0028] When online ride-hailing drivers have medium-level authority, they will receive medium-level order dispatch priority and incentives;

[0029] When an online ride-hailing driver has high-level permissions, he or she will receive high-level order dispatch priority and incentives.

[0030] A different SP online car-hailing driver authority control system, including:

[0031] A data collection module is used to collect driver behavior parameters and marketing behavior parameters of drivers participating in the platform from historical order data of N online ride-hailing vehicles. The driver behavior parameters include order acceptance rate, punctuality rate and user evaluation coefficient, and the marketing behavior parameters include activity participation rate and platform activity, and establish driver behavior parameter data sets and marketing behavior parameter data sets respectively;

[0032] A data processing module, used to dedimensionalize the driver behavior parameter data set and the marketing behavior parameter data set, and perform correlation analysis to generate a driver behavior coefficient and a marketing behavior coefficient respectively;

[0033] A data analysis module, for de-dimensionalizing the driver behavior coefficient and the marketing behavior coefficient, and performing correlation analysis to generate a comprehensive coefficient for characterizing the authority level of the online car-hailing driver, and comparing the comprehensive coefficient of the online car-hailing driver with a set comprehensive coefficient threshold to determine the interval range of the comprehensive coefficient of the online car-hailing driver and the corresponding assigned authority level;

[0034] The authority allocation module is used to obtain corresponding permissions according to the authority level allocated by the online car-hailing driver.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] The present invention collects driver behavior parameters and platform marketing behavior parameters in the historical order data of online car-hailing, and establishes driver behavior parameter data sets and marketing behavior parameter data sets, processes and analyzes the driver behavior parameter data sets and the marketing behavior parameter data sets, generates driver behavior coefficients and marketing behavior coefficients respectively, processes and analyzes the driver behavior coefficients and the marketing behavior coefficients, generates a comprehensive coefficient for characterizing the authority level of the online car-hailing driver, and compares the comprehensive coefficient of the online car-hailing driver with the set comprehensive coefficient threshold, determines the interval range of the comprehensive coefficient of the online car-hailing driver and the corresponding assigned authority level, and obtains the corresponding authority according to the authority level assigned to the online car-hailing driver. Therefore, by collecting and analyzing the driver's behavior parameter data and participating in the platform marketing behavior parameters, the system can more accurately evaluate the behavior performance of each driver, and according to the behavior performance of each driver, perform fair and accurate driver authority allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a schematic diagram of the overall method flow of the present invention;

[0038] Figure 2 It is a block diagram of the module composition of the present invention. DETAILED DESCRIPTION

[0039] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0040] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0041] Example:

[0042] See also Figure 1 to Figure 2 , the present invention provides a technical solution:

[0043] A method for controlling the authority of different SP online car-hailing drivers, such as Figure 1 As shown, the specific steps include:

[0044] S1. Collect driver behavior parameters and marketing behavior parameters of drivers participating in the platform from historical order data of N online ride-hailing vehicles, where the driver behavior parameters include order acceptance rate, punctuality rate and user evaluation coefficient, and the marketing behavior parameters include activity participation rate and platform activity, and establish driver behavior parameter data sets and marketing behavior parameter data sets respectively;

[0045] S2. Dedimensionalize the driver behavior parameter data set and the marketing behavior parameter data set, and perform correlation analysis to generate a driver behavior coefficient and a marketing behavior coefficient respectively;

[0046] S3. De-dimensionalize the driver behavior coefficient and the marketing behavior coefficient, and perform correlation analysis to generate a comprehensive coefficient for characterizing the authority level of the online car-hailing driver, and compare the comprehensive coefficient of the online car-hailing driver with the set comprehensive coefficient threshold to determine the interval range of the comprehensive coefficient of the online car-hailing driver and the corresponding assigned authority level;

[0047] S4. Obtain corresponding permissions according to the permission level assigned by the online car-hailing driver.

[0048] When the driver's order acceptance rate is different, the permissions allocated to the online car-hailing driver are different. The specific reasons are as follows:

[0049] Efficiency optimization: Online ride-hailing platforms tend to assign more orders to drivers with a high acceptance rate, because these drivers are more likely to accept orders. This improves overall service efficiency and reduces passenger waiting time.

[0050] Incentive mechanism: A higher acceptance rate may be seen as a positive performance of the driver. Therefore, the platform may incentivize these drivers by providing more order opportunities or more priority order allocation.

[0051] Service quality management: A low acceptance rate may indicate that drivers are selective in accepting orders, which may affect the passenger experience. Therefore, the platform may ensure service quality by reducing the order allocation of these drivers.

[0052] Demand and supply balance: During high-demand periods, the platform may prioritize drivers with high acceptance rates to ensure a quick response to passenger requests. During lower-demand periods, this difference may be less significant.

[0053] Algorithm optimization: The allocation algorithm of online ride-hailing platforms may optimize order allocation based on historical data and driver performance (including order acceptance rate) to improve overall service level and efficiency.

[0054] Therefore, monitoring the acceptance rate of online car-hailing drivers through the backend or backend server is particularly important for monitoring the allocation of permissions for online car-hailing. If this can be done, the following effects will occur:

[0055] Real-time performance monitoring: The backend can monitor the operating status of the online car-hailing system in real time, including indicators such as order acceptance rate and response time. This helps to promptly discover and solve system performance problems and improve user experience.

[0056] Intelligent scheduling optimization: By monitoring the order acceptance rate, the backend can perform intelligent scheduling optimization. For example, according to the order acceptance rate in different time periods, vehicle resources can be reasonably allocated to improve overall operational efficiency.

[0057] Cost savings: By monitoring the acceptance rate of online ride-hailing services, we can better understand the peak and valley periods of demand and arrange personnel and vehicles appropriately, thereby reducing resource waste and lowering operating costs.

[0058] Improved user experience: The optimized system can better meet user needs, increase order acceptance rate, shorten waiting time, thereby improving user experience and enhancing user satisfaction.

[0059] When the proportion of on-time rate of online ride-hailing vehicles is different, the permissions allocated to the online ride-hailing vehicles are different. The specific reasons are as follows:

[0060] When online ride-hailing drivers show a high punctuality rate during a specific time period, the platform may give them more order allocation rights, because a high punctuality rate usually means that the driver is more reliable and can meet the needs of passengers on time, improving the quality of service. On the contrary, for drivers with a low punctuality rate, the platform may limit their order allocation rights to ensure the maintenance of the overall service level and user experience.

[0061] Therefore, monitoring the punctuality of online ride-hailing vehicles through the backend or backend server is particularly important for monitoring the authority allocation of online ride-hailing drivers. If this can be done, the following effects will be achieved:

[0062] Adjust resource allocation in real time: Backend monitoring of punctuality allows the platform to understand the performance of different drivers in real time. Based on this data, the platform can dynamically adjust resource allocation, prioritize orders to drivers with high punctuality rates, and improve overall service efficiency.

[0063] Improving service quality: Monitoring punctuality helps improve the overall quality of online ride-hailing services. By allocating more orders to drivers with high punctuality rates, the platform can ensure that passengers have easier access to reliable services, thereby improving the user experience.

[0064] Implementing a reward and punishment mechanism: Based on the monitoring results, the platform can implement a reward and punishment mechanism. For example, drivers with high punctuality rates can be given additional incentives, while drivers with low punctuality rates may have their permissions restricted or be provided with training to improve their performance.

[0065] Optimizing resources: By monitoring on-time performance, the platform can better optimize resources. During a specific time period, more orders can be allocated to drivers who have performed well in the past to adapt to different peaks and valleys in demand and improve the overall efficiency of the network.

[0066] Build user trust: By continuously monitoring and optimizing on-time performance, platforms can build user trust in their services. Users are more likely to choose platforms that are based on reliability, which in turn encourages drivers to maintain a high on-time performance.

[0067] When the user evaluation coefficient of the online car-hailing driver is different, the permissions allocated to the online car-hailing driver are different. The specific reasons are as follows:

[0068] Evaluation of service quality: The user evaluation coefficient is an important indicator reflecting the driver's service quality. A high evaluation coefficient usually means that the driver performs well in the service, so the platform may give priority to assigning more orders to improve the overall service quality.

[0069] Optimization of user experience: Online ride-hailing platforms hope to provide high-quality user experience, and user evaluation is a key feedback mechanism. By adjusting permissions based on evaluation coefficients, the platform can ensure that users are more likely to be assigned to drivers with higher service levels, thereby improving user experience.

[0070] Build trust and loyalty: A high rating coefficient helps build user trust in the platform and drivers. By giving highly rated drivers more allocation authority, the platform can increase user confidence in the service, increase their loyalty, and make them more inclined to choose the platform.

[0071] Driver service level: The user evaluation coefficient is directly related to authority and may become a driving force to motivate drivers to improve their service level. A high evaluation coefficient brings more orders, and drivers may be more inclined to provide better service to maintain a high evaluation.

[0072] Platform reputation: User reviews are directly related to the reputation of the platform. By limiting the permissions of low-rated drivers, the platform can maintain its good reputation and avoid affecting its overall image due to poor service.

[0073] Network congestion: Avoiding giving too many orders to low-rated drivers can help reduce network congestion and poor service, thereby improving overall efficiency.

[0074] Therefore, monitoring the user evaluation coefficient of online car-hailing drivers through the backend or backend server is particularly important for monitoring the authority allocation of online car-hailing. If this can be done, the following effects will be produced:

[0075] Improve service level: By monitoring the user evaluation coefficient, the platform can identify drivers with good performance and give them priority in assigning orders. This helps improve the overall service level, because highly rated drivers tend to provide a better ride experience.

[0076] Incentivize drivers to improve service quality: User reviews are directly linked to order allocation authority. Drivers are more motivated to improve service quality and strive for higher reviews, thereby obtaining more orders.

[0077] Improve the effective use of resources: Prioritizing orders to drivers with high ratings can make more effective use of platform resources, increase order completion rates, reduce user waiting time, and optimize overall operational efficiency.

[0078] Increase service volume during peak hours: During peak hours, by selectively allocating orders to drivers with high ratings, the platform can better cope with high traffic conditions and ensure that service is not excessively delayed.

[0079] When the activity participation rate of online ride-hailing drivers is different, the permissions allocated to the online ride-hailing drivers are different. The specific reasons are as follows:

[0080] Supply and demand: Drivers with high activity participation rates usually indicate that they are active and willing to accept more orders. The platform may adjust permissions based on activity participation rates to balance service supply and demand and ensure that highly active drivers can receive orders more frequently.

[0081] Resource allocation principle: By monitoring the activity participation rate, the platform can better understand the availability of different drivers. Based on this information, the platform can allocate orders in a targeted manner so that those drivers who are willing to participate more actively get more opportunities, thereby optimizing resource utilization.

[0082] Order response: Giving more permissions to drivers with high activity participation rates can improve order response speed, which is crucial to user experience because users are more willing to choose drivers who can respond quickly rather than drivers with longer waiting times.

[0083] Driver participation: Activity participation rate is directly related to the allocation of authority, which becomes a driving force to motivate drivers to increase their activity, which helps promote competition and improve overall service levels.

[0084] Therefore, monitoring the platform activity of online ride-hailing drivers through the backend or backend server is particularly important for monitoring the authority allocation of online ride-hailing drivers. If this can be done, the following effects will be produced:

[0085] Balancing supply and demand: Drivers with high activity participation rates usually indicate that they are active and willing to accept more orders. The platform may adjust permissions based on activity participation rates to balance service supply and demand and ensure that highly active drivers can receive orders more frequently.

[0086] Optimize resource allocation: By monitoring activity participation rates, the platform can better understand the availability of different drivers. Based on this information, the platform can allocate orders in a targeted manner, so that those drivers who are willing to participate more actively get more opportunities, thereby optimizing resource utilization.

[0087] Improve order response speed: Giving more permissions to drivers with high activity participation rates can improve order response speed, which is crucial to user experience because users are more willing to choose drivers who can respond quickly rather than drivers with longer waiting times.

[0088] Incentivize drivers to increase engagement: Knowing that campaign engagement rates are directly tied to privilege allocations can be a motivator to increase driver activity, which helps foster competition and improve overall service levels.

[0089] To sum up, collecting the order acceptance rate, punctuality rate, user evaluation coefficient, activity participation rate and platform activity of online car-hailing drivers plays an extremely important role in the authority allocated to online car-hailing drivers. The following is a specific implementation method for collecting the order acceptance rate, punctuality rate, user evaluation coefficient, activity participation rate and platform activity of online car-hailing drivers in this implementation method.

[0090] Based on the above embodiment, the collected online car-hailing drivers' order acceptance rate, punctuality rate and user evaluation coefficient are processed in a dimensionless manner and subjected to correlation analysis to generate the driver behavior coefficient. , based on the following formula:

[0091] ;

[0092] in, is the acceptance rate of online car-hailing drivers, The punctuality rate of online ride-hailing drivers, is the user evaluation coefficient of online car-hailing drivers, is the factor coefficient of the online car-hailing driver’s order acceptance rate, is the factor coefficient of the punctuality rate of online car-hailing drivers, is the factor coefficient of the user evaluation coefficient of the online car-hailing driver, , =1.42, is a constant correction factor.

[0093] From the above formula, we can see that when The higher the driver behavior coefficient The higher, The higher the driver behavior coefficient The lower, The higher the driver behavior coefficient The higher the value, the , , and There is a positive correlation, and the factor coefficient is used to balance the proportion of each data in the formula, thereby promoting the accuracy of the calculation results.

[0094] Based on the above embodiment, the activity participation rate and platform activity of online car-hailing drivers collected are processed in a dimensionless manner, and correlation analysis is performed to generate a marketing behavior coefficient. , based on the following formula:

[0095] ;

[0096] in, is the activity participation rate of online car-hailing drivers, The platform activity of online ride-hailing drivers. is the factor coefficient of the activity participation rate of online car-hailing drivers, is the factor coefficient of the platform activity of online car-hailing drivers, , =1.06, is a constant correction factor.

[0097] From the above formula, we can see that when The higher the marketing behavior coefficient The higher, The higher the marketing behavior coefficient The lower the , and There is a positive correlation, and the factor coefficient is used to balance the proportion of each data in the formula, thereby promoting the accuracy of the calculation results.

[0098] Based on the above embodiment, the driver behavior coefficient of the online car-hailing and marketing behavior coefficient Dedimensionalize the data and conduct correlation analysis to generate a comprehensive coefficient for characterizing the authority level of online car-hailing drivers , based on the following formula:

[0099] ;

[0100] in, is the factor coefficient of the driver behavior coefficient of online car-hailing, is the factor coefficient of the marketing behavior coefficient of online car-hailing, , =1.1.

[0101] From the above formula, we can see that when The higher the comprehensive coefficient The higher, The higher the comprehensive coefficient The higher the value, the , and There is a positive correlation, and the factor coefficient is used to balance the proportion of each data in the formula, thereby promoting the accuracy of the calculation results.

[0102] Based on the above embodiment, the comprehensive coefficient of the online car-hailing driver is and the set comprehensive coefficient threshold In comparison, the process of determining the range of the comprehensive coefficient of the online car-hailing driver and the corresponding assigned authority level is as follows:

[0103] when , then the online car-hailing driver is assigned low-level permissions;

[0104] when , then the online car-hailing driver is assigned the middle-level authority;

[0105] when , then high-level permissions will be assigned to the online car-hailing driver.

[0106] Based on the above embodiment, according to the permission level assigned to the online car-hailing driver, the process of obtaining the corresponding permission is as follows:

[0107] When online ride-hailing drivers have low-level permissions, they will receive relatively low order dispatch priority and incentives;

[0108] When online ride-hailing drivers have medium-level authority, they will receive medium-level order dispatch priority and incentives;

[0109] When an online ride-hailing driver has high-level permissions, he or she will receive high-level order dispatch priority and incentives.

[0110] In the formula , , , , , and The specific value of is generally determined by those skilled in the art according to actual conditions. The essence of this formula is a comprehensive analysis by weighted summation. The technicians in this field collect multiple groups of sample data, set corresponding preset proportional coefficients for each group of sample data, substitute the preset proportional coefficients and the collected sample data into the formula, observe the accuracy of the model output and the rationality of the results through repeated experiments and parameter adjustments, gradually adjust these factor coefficients, and compare the performance and effect of the model under different parameter settings to find the optimal coefficient combination, screen the calculated factor coefficients and take the average, and obtain , , , , , and The value of .

[0111] In addition, the size of the preset factor coefficient is to quantify each parameter to obtain a specific value. In order to facilitate subsequent comparison, the size of the coefficient depends on the amount of sample data and the preset proportional coefficient initially set by technical personnel in this field for each set of sample data. It is not unique as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0112] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0113] A different SP online car-hailing driver authority control system, such as Figure 2 As shown, including:

[0114] A data collection module is used to collect driver behavior parameters and marketing behavior parameters of drivers participating in the platform from historical order data of N online ride-hailing vehicles. The driver behavior parameters include order acceptance rate, punctuality rate and user evaluation coefficient, and the marketing behavior parameters include activity participation rate and platform activity, and establish driver behavior parameter data sets and marketing behavior parameter data sets respectively;

[0115] A data processing module is used to de-dimensionalize the driver behavior parameter data set and the marketing behavior parameter data set, and perform correlation analysis to generate a driver behavior coefficient and a marketing behavior coefficient respectively;

[0116] The data analysis module is used to de-dimensionalize the driver behavior coefficient and the marketing behavior coefficient, and perform correlation analysis to generate a comprehensive coefficient for characterizing the authority level of the online car-hailing driver, and compare the comprehensive coefficient of the online car-hailing driver with the set comprehensive coefficient threshold to determine the interval range of the online car-hailing driver's comprehensive coefficient and the corresponding allocated authority;

[0117] The authority allocation module is used to obtain corresponding permissions according to the authority level assigned by the online car-hailing driver.

[0118] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0119] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by computer software, electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software depends on the specific application and design constraints of the technical solution.

[0120] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0121] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A method for controlling the authority of drivers of different SP online car-hailing services, characterized in that: The specific steps include: S1. Collect driver behavior parameters and marketing behavior parameters of drivers participating in the platform from historical order data of N online ride-hailing vehicles, where the driver behavior parameters include order acceptance rate, punctuality rate and user evaluation coefficient, and the marketing behavior parameters include activity participation rate and platform activity, and establish driver behavior parameter data sets and marketing behavior parameter data sets respectively; S2. Dedimensionalizing the driver behavior parameter data set and the marketing behavior parameter data set, and performing correlation analysis to generate a driver behavior coefficient and a marketing behavior coefficient, respectively; S3. Dedimensionalize the driver behavior coefficient and the marketing behavior coefficient, and perform correlation analysis to generate a comprehensive coefficient for characterizing the authority level of the online car-hailing driver, and compare the comprehensive coefficient of the online car-hailing driver with a set comprehensive coefficient threshold to determine the interval range of the comprehensive coefficient of the online car-hailing driver and the corresponding assigned authority level; S4. Obtain corresponding permissions according to the permission level assigned by the online car-hailing driver; The collected acceptance rate, punctuality rate and user evaluation coefficient of the online car-hailing driver are dedimensionalized and subjected to correlation analysis to generate the driver behavior coefficient SXS, based on the following formula: Among them, JDL is the acceptance rate of online car-hailing drivers, ZSL is the punctuality rate of online car-hailing drivers, PXS is the user evaluation coefficient of online car-hailing drivers, α is the factor coefficient of the acceptance rate of online car-hailing drivers, β is the factor coefficient of the punctuality rate of online car-hailing drivers, γ is the factor coefficient of the user evaluation coefficient of online car-hailing drivers, α>β>γ>0, α+β+γ=1.42, C1 is the constant correction coefficient; The collected data on the online car-hailing drivers’ activity participation rate and platform activity are undimensionalized and subjected to correlation analysis to generate the marketing behavior coefficient YXS, based on the following formula: Among them, HCL is the activity participation rate of online car-hailing drivers, HYD is the platform activity of online car-hailing drivers, δ is the factor coefficient of the activity participation rate of online car-hailing drivers, ε is the factor coefficient of the platform activity of online car-hailing drivers, δ>ε>0, δ+ε=1.06, C2 is the constant correction coefficient; The driver behavior coefficient SXS and marketing behavior coefficient YXS of the online car-hailing vehicle are dedimensionalized and subjected to correlation analysis to generate a comprehensive coefficient ZXS for characterizing the authority level of the online car-hailing vehicle driver, based on the following formula: Among them, ∈ is the factor coefficient of the driver behavior coefficient of the online car-hailing service, θ is the factor coefficient of the marketing behavior coefficient of the online car-hailing service, ∈>θ>0, ∈+θ=1.1; The process of comparing the comprehensive coefficient ZXS of the online car-hailing driver with the set comprehensive coefficient threshold YZ to determine the interval range of the comprehensive coefficient of the online car-hailing driver and the corresponding assigned authority level is as follows: When 0<ZXS≤0.5YZ, the low-level authority is assigned to the online car-hailing driver; When 0.5YZ<ZXS≤YZ, the middle-level authority is assigned to the online car-hailing driver; When YZ<ZXS, the online car-hailing driver is assigned high-level permissions.

2. A method for controlling the authority of drivers of different SP online car-hailing services according to claim 1, characterized in that: According to the permission level assigned to the online car-hailing driver, the process of obtaining the corresponding permission is as follows: When online ride-hailing drivers have low-level permissions, they will receive relatively low order dispatch priority and incentives; When online ride-hailing drivers have medium-level authority, they will receive medium-level order dispatch priority and incentives; When an online ride-hailing driver has high-level permissions, he or she will receive high-level order dispatch priority and incentives.

3. A system for controlling the authority of different SP online car-hailing drivers, the system being used to execute the method for controlling the authority of different SP online car-hailing drivers as described in any one of claims 1-2, characterized in that: include: A data collection module is used to collect driver behavior parameters and marketing behavior parameters of drivers participating in the platform from historical order data of N online ride-hailing vehicles. The driver behavior parameters include order acceptance rate, punctuality rate and user evaluation coefficient, and the marketing behavior parameters include activity participation rate and platform activity, and establish driver behavior parameter data sets and marketing behavior parameter data sets respectively; A data processing module, used to dedimensionalize the driver behavior parameter data set and the marketing behavior parameter data set, and perform correlation analysis to generate a driver behavior coefficient and a marketing behavior coefficient respectively; A data analysis module, for de-dimensionalizing the driver behavior coefficient and the marketing behavior coefficient, and performing correlation analysis to generate a comprehensive coefficient for characterizing the authority level of the online car-hailing driver, and comparing the comprehensive coefficient of the online car-hailing driver with a set comprehensive coefficient threshold to determine the interval range of the comprehensive coefficient of the online car-hailing driver and the corresponding allocated authority; The authority allocation module is used to obtain corresponding permissions according to the authority level allocated by the online car-hailing driver.

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